Remote Sensing Of Rice-Based Irrigated Agriculture: A Review

The ‘Green Revolution’ in rice farming of the late 1960’s denotes the beginning of the extensive breeding programs that have led to the many improved rice varieties that are now planted on more than 60% of the world’s riceland (Khush, 1987). This revolution led to increases in yield potential of 2 to 3 times that of traditional varieties (Khush, 1987). Similar trends have also been seen in the Irrigation Areas and Districts of southern New South Wales (NSW) as the local breeding program has produced many improved varieties of rice adapted to local growing conditions since the 1960’s (Brennan et al., 1994). Increases in area of rice planted, rice quality, and paddy yield resulted (Brennan et al., 1994). Increased rice area, however, has led to the development of high water tables and risk of large tracts of land becoming salt-affected in southern NSW (Humphreys et al., 1994b). These concerns have led to various environmental regulations on rice in the region, culminating in 1994 when restrictions on rice area, soil suitability, and water consumption were fully enacted (Humphreys et al., 1994b). Strict environmental restrictions in combination with large areas of land make the management of this region a difficult task. Land managers require, among other things, a way of regulating water use, assessing or predicting crop area and productivity, and making management decisions in support of environmentally and economically sustainable agriculture. In the search for more time and cost effective methods for attaining these goals, while monitoring complex management situations, many have turned to remote sensing and Geographic Information System (GIS) technologies for assistance. The spectral information and spatial density of remote sensing data lends itself well to the measurement of large areas. Since the launch of LANDSAT-1 in 1972, this technology has been used extensively in agricultural systems for crop identification and area estimation, crop yield estimation and prediction, and crop damage assessment. The incorporation of remote sensing and GIS can also help integrate management practices and develop effective management plans. However, in order to take advantage of these tools, users must have an understanding of both what remote sensing is and what sensors are now available, and how the technology is being used in applied agricultural research. Accordingly, a description of both follows: first a description of the technology, and then how it is currently being applied. The applications of remote sensing relevant to this discussion can be separated into crop type identification; crop area measurement; crop yield; crop damage; water use/ moisture availability (ma) mapping; and water use efficiency monitoring/mapping. This report focuses on satellite remote sensing for broad-scale rice-based irrigation agricultural applications. It also discusses related regional GIS analyses that may or may not include remote sensing data, and briefly addresses other sources of finer-scale remote sensing and geospatial data as they relate to agriculture. Since a complete review of the remote sensing research was not provided in the rice literature alone, some generic agricultural issues have been learned from applications not specifically dealing with rice. Remote sensing specialists may wish to skip to section 2

Assessing and Improving Positional Accuracy and its Effects on Areal Estimation at Coleambally Irrigation Area

If management decisions are made with geospatial data that have not been assessed for positional accuracy, then debate about both methodologies of measurement and management decisions can occur. This debate, in part, can be avoided by assessing the positional accuracy of geospatial data, leading to increased confidence (decreased uncertainty) in both the data and the decisions made from the data. In this study, we assessed the positional accuracy of two Geographic Information System (GIS) baseline datasets at the Coleambally Irrigation Area (CIA); high-resolution digital aerial photography acquired in January 2000, and the Digital Topographic Data Base (DTDB) roads data. We also assessed areal error of paddock measurements from an improved accuracy version of the high-resolution digital aerial photography. Positional accuracies were assessed by comparing well-defined features from both baseline datasets (original aerial photography and DTDB roads) to high-level accuracy Differential Global Positioning System (DGPS) data for the same features. This assessment showed that neither baseline dataset met the National Mapping Council of Australia’s standards of map accuracy. Consequently, we processed the original digital photography to create an improved dataset, which was over 2.5 times more accurate than the original photography, and over 4 times more accurate than the DTDB data. The improved dataset also met the map accuracy standard for Australia. We also assessed areal error by comparing paddock boundaries delineated from the improved dataset to those delineated from a DGPS associated with paddock soil surveys. The 90% confidence interval measured from the improved data for any individual paddock is approximately at the ± 5% target error set by Coleambally Irrigation Limited (CIL). The 95% confidence interval is roughly ± 6%. Overall areal error of multiple paddocks is much lower than the individual case with the 95% confidence interval for 2 paddocks being from about ± 4% error reducing to less than ± 2% for 8 or more paddocks. Knowledge of both positional and areal accuracies of the improved high-resolution digital aerial photography provides a means to more effectively manage environmental compliance of rice farmers at CIA and gives the CIL justification for making management decisions from this spatial data

Assessing the accuracy of blending Landsat-MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection

Blending algorithms model land cover change by using highly resolved spatial data from one sensor and highly resolved temporal data from another. Because the data are not usually observed concurrently, unaccounted spatial and temporal variances cause error in blending algorithms, yet, to date, there has been no definitive assessment of algorithm performance against spatial and temporal variances. Our objectives were to: (i) evaluate the accuracy of two advanced blending algorithms (STARFM and ESTARFM) and two simple benchmarking algorithms in two landscapes with contrasting spatial and temporal variances; and (ii) synthesise the spatial and temporal conditions under which the algorithms performed best. Landsat-like images were simulated on 27 dates in total using the nearest temporal cloud-free Landsat-MODIS pairs to the simulation date, one before and one after. RMSD, bias, and r2 estimates between simulated and observed Landsat images were calculated, and overall variance of Landsat and MODIS datasets were partitioned into spatial and temporal components. Assessment was performed over the whole study site, and for specific land covers. Results addressing objective (i) were that: ESTARFM did not always produce lower errors than STARFM; STARFM and ESTARFM did not always produce lower errors than simple benchmarking algorithms; and land cover spatial and temporal variances were strongly associated with algorithm performance. Results addressing objective (ii) indicated ESTARFM was superior where/when spatial variance was dominant; and STARFM was superior where/when temporal variance was dominant. We proposed a framework for selecting blending algorithms based on partitioning variance into the spatial and temporal components and suggested that comparing Landsat and MODIS spatial and temporal variances was a practical method to determine if, and when, MODIS could add value for blending

Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of “Index-then-Blend” and “Blend-then-Index” Approaches

The objective of this paper was to evaluate the accuracy of two advanced blending algorithms, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) indices to the spatial resolution of Landsat. We tested two approaches: (i) "Index-then-Blend" (IB); and (ii) "Blend-then-Index" (BI) when simulating nine indices, which are widely used for vegetation studies, environmental moisture assessment and standing water identification. Landsat-like indices, generated using both IB and BI, were simulated on 45 dates in total from three sites. The outputs were then compared with indices calculated from observed Landsat data and pixel-to-pixel accuracy of each simulation was assessed by calculating the: (i) bias; (ii) R; and (iii) Root Mean Square Deviation (RMSD). The IB approach produced higher accuracies than the BI approach for both blending algorithms for all nine indices at all three sites. We also found that the relative performance of the STARFM and ESTARFM algorithms depended on the spatial and temporal variances of the Landsat-MODIS input indices. Our study suggests that the IB approach should be implemented for blending of environmental indices, as it was: (i) less computationally expensive due to blending single indices rather than multiple bands; (ii) more accurate due to less error propagation; and (iii) less sensitive to the choice of algorithm

Challenges and directions in studying cell-cell communication by extracellular vesicles

Extracellular vesicles (EVs) are increasingly recognized as important mediators of intercellular communication. They have important roles in numerous physiological and pathological processes, and show considerable promise as novel biomarkers of disease, as therapeutic agents and as drug delivery vehicles. Intriguingly, however, understanding of the cellular and molecular mechanisms that govern the many observed functions of EVs remains far from comprehensive, at least partly due to technical challenges in working with these small messengers. Here, we highlight areas of consensus as well as contentious issues in our understanding of the intracellular and intercellular journey of EVs: from biogenesis, release and dynamics in the extracellular space, to interaction with and uptake by recipient cells. We define knowledge gaps, identify key questions and challenges, and make recommendations on how to address these

The implications of carbon dioxide and methane exchange for the heavy mitigation RCP2.6 scenario under two metrics

Greenhouse gas emissions associated with Representative Concentration Pathway RCP2.6 could limit global warming to around or below a 2 °C increase since pre-industrial times. However this scenario implies very large and rapid reductions in both carbon dioxide (CO2) and non-CO2 emissions, and suggests a need to understand available flexibility between how different greenhouse gases might be abated. There is a growing interest in developing a greater understanding of the particular role of shorter lived non-CO2 gases as abatement options. We address this here through a sensitivity study of different methane (CH4) emissions pathways to year 2100 and beyond, by including exchanges with CO2 emissions, and with a focus on related climate and economic advantages and disadvantages. Metrics exist that characterise gas equivalence in terms of climate change effect per tonne emitted. We analyse the implications of CO2 and CH4 emission exchanges under two commonly considered metrics: the 100-yr Global Warming Potential (GWP-100) and Global Temperature Potential (GTP-100). This is whilst keeping CO2-equivalent emissions pathways fixed, based on the standard set of emissions usually associated with RCP2.6. An idealised situation of anthropogenic CH4 emissions being reduced to zero across a period of two decades and with the implementation of such cuts starting almost immediately gives lower warming than for standard RCP2.6 emissions during the 21st and 22nd Century. This is despite exchanging for higher CO2 emissions. Introducing Marginal Abatement Cost (MAC) curves provides an economic assessment of alternative gas reduction strategies. Whilst simpler than utilising full Integrated Assessment Models (IAMs), MAC curves are more transparent for illustrative modelling. The GWP-100 metric places a relatively high value on climate change prevented for methane emission reduction, as compared to an equivalent mass of CO2 reduction. This in combination with the strong non-linearity in MAC curves (moving quickly from relatively cheap removal to emissions difficult to cut at any cost) causes little change under cost minimisation from standard RCP2.6 emissions. This reflects the original development of RCP2.6 standard emissions from similar minimisation. With gas exchange under GTP-100, however, we find much less methane is abated, resulting in higher temperatures, whilst costs are slightly lower. Our results also highlight the point at which greater methane mitigation would become beneficial from both a climate and economic aspect. If by 2030 removal of all methane were to become possible at an average cost less than $1000 per tonne of CH4, then this would be the cheapest option, for GWP-100 metric and our CO2 MAC curve. Critically this would increase the possibility of constraining warming to two degrees

Down-Regulation of miR-92 in Human Plasma Is a Novel Marker for Acute Leukemia Patients

BACKGROUND: MicroRNAs are a family of 19- to 25-nucleotides noncoding small RNAs that primarily function as gene regulators. Aberrant microRNA expression has been described for several human malignancies, and this new class of small regulatory RNAs has both oncogenic and tumor suppressor functions. Despite this knowledge, there is little information regarding microRNAs in plasma especially because microRNAs in plasma, if exist, were thought to be digested by RNase. Recent studies, however, have revealed that microRNAs exist and escape digestion in plasma. METHODOLOGY/PRINCIPAL FINDINGS: We performed microRNA microaray to obtain insight into microRNA deregulation in the plasma of a leukemia patient. We have revealed that microRNA-638 (miR-638) is stably present in human plasmas, and microRNA-92a (miR-92a) dramatically decreased in the plasmas of acute leukemia patients. Especially, the ratio of miR-92a/miR-638 in plasma was very useful for distinguishing leukemia patients from healthy body. CONCLUSIONS/SIGNIFICANCE: The ratio of miR-92a/miR-638 in plasma has strong potential for clinical application as a novel biomarker for detection of leukemia

Bioclimatological mapping tackling uncertainty propagation: application to mainland Portugal

We present a bioclimatological diagnosis of mainland Portugal, namely the thermotype and ombrotype maps following Rivas-Martínez’s worldwide bioclimatic classi$cation system. In order to obtain this diagnosis, we produced maps of bioclimatological indices using, as base data, geostatistical interpolations of air temperature and precipitation.We performed uncertainty propagation obtaining uncertainty measures for the produced maps: mean absolute errors and root mean squared errors. For the non-linear indices, besides the usual approximation using Taylor expansion, we devised error formulae, for which we showed that the propagated uncertainties are upper bounds on the true uncertainty measures. We compared the obtained uncertainty measures to those reported on a previously published work, which used a different methodological framework to obtain the same diagnosis. Although the approach we used here implies a great number of interpolations and subsequent calculation steps, it permitted the use of a large amount of data relative to precipitation. An F-test showed that the estimated mean squared errors for the maps of ombrothermic indices were signi$cantly lower than those produced by the former methodological framework.info:eu-repo/semantics/publishedVersio

Tumor-derived exosomes confer antigen-specific immunosuppression in a murine delayed-type hypersensitivity model

Exosomes are endosome-derived small membrane vesicles that are secreted by most cell types including tumor cells. Tumor-derived exosomes usually contain tumor antigens and have been used as a source of tumor antigens to stimulate anti-tumor immune responses. However, many reports also suggest that tumor-derived exosomes can facilitate tumor immune evasion through different mechanisms, most of which are antigen-independent. In the present study we used a mouse model of delayed-type hypersensitivity (DTH) and demonstrated that local administration of tumor-derived exosomes carrying the model antigen chicken ovalbumin (OVA) resulted in the suppression of DTH response in an antigen-specific manner. Analysis of exosome trafficking demonstrated that following local injection, tumor-derived exosomes were internalized by CD11c+ cells and transported to the draining LN. Exosome-mediated DTH suppression is associated with increased mRNA levels of TGF-β1 and IL-4 in the draining LN. The tumor-derived exosomes examined were also found to inhibit DC maturation. Taken together, our results suggest a role for tumor-derived exosomes in inducing tumor antigen-specific immunosuppression, possibly by modulating the function of APCs. © 2011 Yang et al